Image processing systems which recognize faces in images and videos typically operate by extracting facial features from the images and applying template matching or classification. In the case of template matching a library of templates is available with each template being annotated as representing the face of a specified person. When extracted facial features from an incoming image match a particular template the system recognizes the face of the person associated with the template. In the case of classification an automated classifier such as a neural network is trained in advance using huge quantities of images depicting faces of specified people. In order to annotate the templates or annotate the training images significant time and expense is involved. These types of face recognition systems work well in controlled environments where the lighting is good and the person is facing the camera but are often not robust where lighting changes, occlusion, and different camera viewpoints occur.
Existing face recognition systems do not behave or operate in the same way as a human does. As a result the functionality of such face recognition systems is limited as compared with a human who is trying to recognize individuals. Also, because existing face recognition systems do not behave or operate in the same way as a human does the existing face recognition systems are not intuitive to use or integrate with other automated systems.
The embodiments described below are not limited to implementations which solve any or all of the disadvantages of known image processing systems for person recognition.